The energy present in a magnetostatic structure has been used in a wide range of applications, including magnetic sensors utilized in a variety of technical and commercial applications, including in particular, automotive applications. The generation of a voltage in a conductor by the changing of a magnetostatic structure or the movement of a magnetostatic structure relative to the conductor is a well-known concept. The aforementioned conductors commonly use a permanent magnet made of electrically conducting metal.
In the development of magnetic sensors, it is often necessary to create various types of magnetic sensor data processing algorithms and systems capable of localizing, quantifying, and classifying such objects based on their magnetostatic fields. In general, a magnetostatic field may be generated by any combination of three physical phenomena: permanent or remanent magnetization, magnetostatic induction, and electromagnetic induction. The first phenomena can occur in objects that contain metals of the ferromagnetic group, which includes iron, nickel, cobalt, and their alloys. These may be permanently magnetized either through manufacture or use. Second, fields external to the object may induce a secondary field in ferromagnetic structures and also paramagnetic structures if the mass and shape sufficiently enhance the susceptibility. Third, the object may comprise a large direct current loop that induces its own magnetic field.
Many of the current magnetostatic modeling methods applicable to magnetic sensor development rely upon boundary element modeling software. Such software generally utilizes both direct and indirect boundary element methods as well as finite element methods to accurately model magnetic properties of various structures/solids and their interaction with surrounding components. The boundary element method has become an important technique in the computational solution of a number of physical problems. In common with the better-known finite element method and finite difference method, the boundary element method is essentially a mathematical and algorithmic technique that can be utilized to solve partial differential equations. Boundary element techniques generally have earned the important distinction that only the boundary of the domain of interest requires discretisation. For example, if the domain is either the interior or exterior to a sphere, then the resulting diagram will depict a typical mesh, and only the surface is generally divided into elements. Thus, the computational advantages of the boundary element technique over other methods can be considerable, particularly in magnetic modeling applications.
One particular modeling software and its associated algorithms and software modules that have been utilized by the present inventor at Honeywell is referred to collectively as “Narfmm”. Such magnet models generally have always assumed a relative permeability (i.e., μ in units of Gauss/Oersted) equal to one. This assumption results in magnet models with “magnetic charge” residing only on the surface of the magnet. This is a good approximation for magnet materials such as sintered SmCo and NdFeB that have μ between 1 and 1.1. For magnet materials, however, with a greater μ, such as NdFeB powder in plastic with μ equal to 1.3, assuming μ equal to one, large errors typically can result. Also for nonlinear BH curve materials, such as AlNiCo, magnetostatic models such as Narfmm models can result in a substantial error. For ferrous objects, magnetostatic models such as Narfmm generally assume infinite permeability, which forces the “magnetic charge” to reside only on the surface of the ferrous object. If this is not a good assumption, software models such as Narfmm models have been inadequate.
Based on the foregoing, the present inventor has concluded that a more accurate model for magnets and ferrous objects should have a varying surface charge density at the magnetic object surface and also a varying magnetic charge distributed throughout the volume of the magnetic object. The present invention thus improves modeling accuracy by describing a method for modeling magnets and ferrous objects, which have any defined BH curve within modeling methods, such as, for example, the Narfmm software, thereby eliminating prior art limitations.